Read e-book online Action Rules Mining (Studies in Computational Intelligence, PDF

By Agnieszka Dardzinska

ISBN-10: 3642356508

ISBN-13: 9783642356506

We're surrounded by way of info, numerical, express and in a different way, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids knowing and determination making. information analysts in lots of disciplines similar to company, schooling or medication, are often requested to investigate new facts units that are frequently composed of various tables owning various homes. they fight to discover thoroughly new correlations among attributes and exhibit new percentages for users.

Action ideas mining discusses a few of facts mining and information discovery ideas after which describe consultant recommendations, tools and algorithms attached with motion. the writer introduces the formal definition of motion rule, suggestion of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and offers a method how you can build easy organization motion principles of a lowest rate. a brand new technique for producing motion ideas from datasets with numerical attributes via incorporating a tree classifier and a pruning step in line with meta-actions is additionally provided. during this e-book we will locate basic recommendations precious for designing, utilizing and enforcing motion principles to boot. precise algorithms are supplied with beneficial clarification and illustrative examples.

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Additional resources for Action Rules Mining (Studies in Computational Intelligence, Volume 468)

Example text

2. If t1 , t2 are action terms, then t1 ∗ t2 is an action term. 3. If t is an action term containing (a, a1 → a2 ), (b, b1 → b2 ) as its subterms, then a = b. 3. By the domain of an action term t, denoted by Dom(t), we mean the set of all attribute names listed in t. 4. By an action rule we mean an expression r = [t1 → t2 ], where t1 is an action term, and t2 is an atomic action term. Additionally we assume, that Dom(t2 ) = {d} and Dom(t1 ) ⊆ A, where A is a set of attributes. The domain Dom(r) of action rule r is defined as Dom(t1 ) ∪ Dom(t2 ).

37) - not marked - marked negative - not marked We obtained seven marked positive rules, where support and threshold hold, and nine rules, where only support holds. 5. We propose the following definition for concatenating any two sets c∗i = {xi , pi }i∈N , e∗j = {yj , qj }j∈M where K = M ∩N : (ci ·ej )∗ = {(xi , pi ·qj )}i∈K . 5 ) (c1 , d2 )∗ ⊆ e∗3 (sup = 12 < 1) - marked positive - marked negative All sets were marked, therefore we can already start to extract rules from an incomplete information system.

Otherwise, the confidence of the rule ci → ej is checked. If that confidence is either above or equals the assumed threshold value, the rule is approved and the corresponding relationship {xi , pi }i∈N ⊆ {yj , qj }j∈M is marked. Otherwise this corresponding relationship remains unmarked. 37) - not marked - marked negative - not marked We obtained seven marked positive rules, where support and threshold hold, and nine rules, where only support holds. 5. We propose the following definition for concatenating any two sets c∗i = {xi , pi }i∈N , e∗j = {yj , qj }j∈M where K = M ∩N : (ci ·ej )∗ = {(xi , pi ·qj )}i∈K .

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Action Rules Mining (Studies in Computational Intelligence, Volume 468) by Agnieszka Dardzinska


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